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EMPIRICAL-LIKELIHOOD-BASED CONFIDENCE INTERVALS FOR CONDITIONAL VARIANCE IN HETEROSKEDASTIC REGRESSION MODELS

Published online by Cambridge University Press:  30 April 2010

Ngai Hang Chan,
Liang Peng and
Dabao Zhang
Ngai Hang Chan*
Affiliation:
The Chinese University of Hong Kong
Liang Peng
Affiliation:
Georgia Institute of Technology
Dabao Zhang
Affiliation:
Purdue University
*
*Address correspondence to Ngai Hang Chan, Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; email: nhchan@sta.cuhk.edu.hk.
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Abstract

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Fan and Yao (1998) proposed an efficient method to estimate the conditional variance of heteroskedastic regression models. Chen, Cheng, and Peng (2009) applied variance reduction techniques to the estimator of Fan and Yao (1998) and proposed a new estimator for conditional variance to account for the skewness of financial data. In this paper, we apply empirical likelihood methods to construct confidence intervals for the conditional variance based on the estimator of Fan and Yao (1998) and the reduced variance modification of Chen et al. (2009). Simulation studies and data analysis demonstrate the advantage of the empirical likelihood method over the normal approximation method.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 27 , Issue 1: SPECIAL ISSUE ON EMPIRICAL LIKELIHOOD AND RELATED METHODS , February 2011, pp. 154 - 177
Copyright
Copyright © Cambridge University Press 2010

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